论文标题
使用功能协变量的观察性研究中的平均治疗效应估计
Average Treatment Effect Estimation in Observational Studies with Functional Covariates
论文作者
论文摘要
功能数据分析是现代统计数据中的重要领域，并且已成功应用于许多领域。尽管许多科学研究旨在寻找因果关系，但主要的功能数据分析方法仅能揭示相关性。在本文中，研究了具有功能协变量的观察数据的平均治疗效果估计。本文将多元数据的各种最先进的倾向得分估计方法推广到功能数据。通过模拟研究对倾向评分加权进行的平均治疗效应估计量进行数值评估，并应用于现实世界数据集，以研究杜洛西汀对慢性膝关节骨关节炎患者疼痛缓解疼痛的因果作用。
Functional data analysis is an important area in modern statistics and has been successfully applied in many fields. Although many scientific studies aim to find causations, a predominant majority of functional data analysis approaches can only reveal correlations. In this paper, average treatment effect estimation is studied for observational data with functional covariates. This paper generalizes various state-of-art propensity score estimation methods for multivariate data to functional data. The resulting average treatment effect estimators via propensity score weighting are numerically evaluated by a simulation study and applied to a real-world dataset to study the causal effect of duloxitine on the pain relief of chronic knee osteoarthritis patients.
